Speaker
Irina Espejo Morales
(New York University (US))
Description
Excursion is a python package that efficiently estimates level sets of computationally expensive black box functions. It is a confluence of Active Learning and Gaussian Process Regression. The difference between Level Set Estimation and Bayesian Optimization is that the latter focuses on finding maxima and minima while the former intends to find regions of the parameter space where the function takes a specified value, like a countour.
Excursion uses GPyTorch with state-of-the-art fast posterior fitting techniques and takes advantage of GPUs to scale computations to a higher dimensional input space of the black box function than traditional approaches.
Author
Irina Espejo Morales
(New York University (US))
Co-authors
Kyle Stuart Cranmer
(New York University (US))
Lukas Alexander Heinrich
(CERN)
Patrick Rieck
(Max-Planck-Institut fur Physik (DE))
Prof.
Gilles Louppe
(University of Liege)
